Application of Text-Analytics in Quantitative Study of Science and Technology

Part of the Springer Handbooks book series (SHB)


The quantitative study of science, technology and innovation (ST&I ) has experienced significant growth with advancements in disciplines such as mathematics, computer science and information sciences. From the early studies utilizing the statistics method, graph theory, to citations or co-authorship, the state of the art in quantitative methods leverages natural language processing and machine learning. However, there is no unified methodological approach within the research community or a comprehensive understanding of how to exploit text-mining potentials to address ST&I research objectives. Therefore, this chapter intends to present the state of the art of text mining within the framework of ST&I. The major contribution of the chapter is twofold; first, it provides a review of the literature on how text mining extended the quantitative methods applied in ST&I and highlights major methodological challenges. Second, it discusses two hands-on detailed case studies on how to implement the text analytics routine.

text-mining scientometrics bibliometrics text analytics literature review science mapping natural language processing machine learning 


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© Springer International Publishing AG, part of Springer Nature 2019

Authors and Affiliations

  1. 1.School of Engineering Science, Industrial Engineering and ManagementLappeenranta University of Technology (LUT)LappeenrantaFinland
  2. 2.VTT Technical Research Centre of FinlandEspooFinland
  3. 3.Search Technology, Inc.Norcross, GAUSA

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